Extracting domain-specific actions and entities in natural language commands

ABSTRACT

The present disclosure relates to processing domain-specific natural language commands. An example method generally includes receiving a natural language command. A command processor compares the received natural language command to a corpus of known commands to identify a probable matching command in the corpus of known commands to the received natural language command. The corpus of known commands comprises a plurality of domain-specific commands, each of which is mapped to a domain-specific action. Based on the comparison, the command processor identifies the domain-specific action associated with the probable matching command to perform in response to the received command and executes the identified domain-specific action.

BACKGROUND Field

Embodiments presented herein generally relate to natural languageprocessing, and more specifically to extracting domain-specific actionsand entities in natural language commands.

Description of the Related Art

Natural language processing generally allows users of a softwareapplication to input a command to the software application as a spokenphrase specifying an action to perform and an entity to perform theaction on. These spoken phrases need not be formatted in a specificmanner for the software application to parse the command and identifythe action to be performed and the entity to perform the action on. Whena software application receives a natural language command forprocessing, a set of rules and decisions established for processing thecommand can be used to interpret the natural language command andextract actions to be performed and entities to perform the actions on.These rules may be established manually or through machine learning,where a system that uses natural language processing continuallycreates, removes, and/or modifies the rules for processing naturallanguage commands based on training data sets and corrections to theextracted actions and entities provided by one or more users of thesoftware application.

Typically, natural language processing systems are configured forgeneral computing tasks. For example, natural language processingsystems deployed for use on mobile devices may be configured to processgeneral computing tasks, such as identifying appointments on a user'scalendar, obtaining navigation information to a user-specifieddestination, initiating a phone call, sending text messages, searchingfor information on the internet and other computing tasks that a usercan perform on a mobile device. However, these natural languageprocessing systems are generally not configured to processdomain-specific commands, where actions and entities have specificdefinitions according to the domain in which the command is processed.Thus, there is a need for domain-specific action and entity recognitionin natural language processing systems.

SUMMARY

One embodiment of the present disclosure includes a method forprocessing a domain-specific natural language command. The methodgenerally includes generally includes receiving a natural languagecommand. A command processor compares the received natural languagecommand to a corpus of known commands to identify a probable matchingcommand in the corpus of known commands to the received natural languagecommand. The corpus of known commands comprises a plurality ofdomain-specific commands, each of which is mapped to a domain-specificaction. Based on the comparison, the command processor identifies thedomain-specific action associated with the probable matching command toperform in response to the received command and executes the identifieddomain-specific action.

Another embodiment provides a computer-readable storage medium havinginstructions, which, when executed on a processor, performs an operationfor processing a domain-specific natural language command. The operationgenerally includes generally includes receiving a natural languagecommand. A command processor compares the received natural languagecommand to a corpus of known commands to identify a probable matchingcommand in the corpus of known commands to the received natural languagecommand. The corpus of known commands comprises a plurality ofdomain-specific commands, each of which is mapped to a domain-specificaction. Based on the comparison, the command processor identifies thedomain-specific action associated with the probable matching command toperform in response to the received command and executes the identifieddomain-specific action.

Still another embodiment of the present invention includes a processorand a memory storing a program, which, when executed on the processor,performs an operation for processing a domain-specific natural languagecommand. The operation generally includes generally includes receiving anatural language command. A command processor compares the receivednatural language command to a corpus of known commands to identify aprobable matching command in the corpus of known commands to thereceived natural language command. The corpus of known commandscomprises a plurality of domain-specific commands, each of which ismapped to a domain-specific action. Based on the comparison, the commandprocessor identifies the domain-specific action associated with theprobable matching command to perform in response to the received commandand executes the identified domain-specific action.

One embodiment of the present disclosure includes a method forprocessing a domain-specific natural language command. The methodgenerally includes receiving a natural language command. A commandprocessor compares the received natural language command to a corpus ofknown commands to identify a probable matching command to the receivednatural language command. The corpus of known commands comprises aplurality of domain-specific commands, each of which is mapped to one ormore domain-specific entities. Based on the comparison, the commandprocessor identifies one or more entities in the received naturallanguage command to perform an action on based on the mapping of the oneor more domain-specific entities in the probable matching command andexecutes a domain-specific action included in the natural languagecommand on the identified entity.

Another embodiment provides a computer-readable storage medium havinginstructions, which, when executed on a processor, performs an operationfor processing a domain-specific natural language command. The operationgenerally includes receiving a natural language command. A commandprocessor compares the received natural language command to a corpus ofknown commands to identify a probable matching command to the receivednatural language command. The corpus of known commands comprises aplurality of domain-specific commands, each of which is mapped to one ormore domain-specific entities. Based on the comparison, the commandprocessor identifies one or more entities in the received naturallanguage command to perform an action on based on the mapping of the oneor more domain-specific entities in the probable matching command andexecutes a domain-specific action included in the natural languagecommand on the identified entity.

Still another embodiment provides a processor and a memory storing aprogram, which, when executed on the processor, performs an operationfor processing a domain-specific natural language command. The operationgenerally includes receiving a natural language command. A commandprocessor compares the received natural language command to a corpus ofknown commands to identify a probable matching command to the receivednatural language command. The corpus of known commands comprises aplurality of domain-specific commands, each of which is mapped to one ormore domain-specific entities. Based on the comparison, the commandprocessor identifies one or more entities in the received naturallanguage command to perform an action on based on the mapping of the oneor more domain-specific entities in the probable matching command andexecutes a domain-specific action included in the natural languagecommand on the identified entity.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentdisclosure can be understood in detail, a more particular description ofthe disclosure, briefly summarized above, may be had by reference toembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrate onlyexemplary embodiments and are therefore not to be considered limiting ofits scope, may admit to other equally effective embodiments.

FIG. 1 illustrates an exemplary networked computing environment,according to one embodiment.

FIG. 2 illustrates an exemplary domain-specific natural language trainerthat uses annotated training data to train a natural language processingsystem, according to one embodiment.

FIG. 3 illustrates exemplary operations for training an actionidentifier to recognize domain-specific actions using an annotatedtraining data set, according to one embodiment.

FIG. 4 illustrates exemplary operations for training an entityidentifier to recognize domain-specific entities using an annotatedtraining data set, according to one embodiment.

FIG. 5 illustrates an exemplary request parser for identifying actionsin a domain-specific natural language command, according to oneembodiment.

FIG. 6 illustrates an exemplary request parser for identifying entitiesin a domain-specific natural language command, according to oneembodiment.

FIG. 7 illustrates exemplary operations that may be performed by anatural language processor to identify and execute a domain-specificaction in a domain-specific natural language command, according to oneembodiment.

FIG. 8 illustrates exemplary operations that may be performed by anatural language processor to identify an entity in a domain-specificnatural language command and perform an action against the identifiedentity, according to one embodiment.

FIG. 9 illustrates exemplary system for extracting actions and entitiesfrom domain-specific natural language commands, according to oneembodiment.

DETAILED DESCRIPTION

Natural language processing systems are typically configured to processcommands that are applicable to general computing tasks, such assearching for information on the internet or a local device, sendinge-mail or text messages, pairing wireless devices, and obtainingnavigation instructions to a destination. Thus, natural languageprocessing systems may be unable to understand the significance ofdomain-specific actions and entities in a command. For example, atypical natural language processing system that receives the command,“Pay my XYZ bill” may not be able to determine that a user wants to paya bill (i.e., initiate a funds transfer from one account to satisfy anoutstanding balance on the bill from XYZ). In another example, a typicalnatural language processing system that receives the command, “InvoiceABC for $500” may not be able to determine that a user wants to requestpayment from a counterparty for the specified amount of money.

Aspects of the present disclosure provide a domain-specific naturallanguage processing system that uses a corpus of known commands to allowa natural language processing system to recognize domain-specificactions and entities in received natural language commands. To train anatural language processing system to recognize domain-specific actions,the corpus of known commands may be annotated with information about adomain-specific action associated with each command in the corpus ofknown commands. Further, to train the natural language processing systemto recognize domain-specific entities in a command, the corpus of knowncommands may be annotated with information about a location of adomain-specific entity in a string representing each command (e.g., thenth word in a string) and an entity type associated with each command inthe corpus of known commands. The natural language processing system cancompare a received command to the commands in the corpus of knowncommands and identify probabilistic matches between the received commandand one or more commands in the corpus of known commands. Based on theidentified probabilistic matches, the natural language processing systemidentifies domain-specific actions and entities on which an action is tobe performed, which allows the natural language processing system toprocess domain-specific natural language commands.

FIG. 1 illustrates an exemplary natural language processing system,according to an embodiment. As illustrated, system 100 includes a clientdevice 120, application server 130, and data store 140, connected vianetwork 110.

Client device 120 is included to be representative of a desktopcomputer, a laptop, a tablet, a smartphone, or any other computingdevice. As illustrated, client device 120 generally executes one or moreapplications 122. The applications 122 generally provide an interfacefor a user to input a natural language command for processing. In somecases, the interface may include a voice recorder which captures auser's voice input using one or more microphones integrated into orotherwise connected to client device 120. When application 122 capturesvoice input of a domain-specific command, a voice-to-text converterconverts the spoken natural language command captured by application 122into a text string that application 122 transmits to application server130 for processing and command execution.

Application server 130 generally is trained using a training data set ofa corpus of known commands to extract actions and entities from areceived natural language command. As illustrated, application server130 includes a natural language trainer 132, a request parser 134, and arequest processor 136.

Natural language trainer 132 generally receives one or more trainingdata sets (e.g., training data 142 in data store 140) to generate setsof rules for request parser 134 to use in identifying domain-specificactions and entities in a received natural language command. Generally,natural language trainer 132 uses the training data set to generaterules for scoring a received command against an action associated withthe received command. These rules take into account the content andcontext of commands in the training data set and a comparison of thecontent of each command in the training data set to the tagged actionwith which the commands are associated and/or the tagged entitiesincluded in the commands. To generate rules for request parser 134 toidentify domain-specific actions in a received natural language command,natural language trainer 132 uses a training data set including a corpusof known commands annotated with information identifying one or moredomain-specific action words in each command and a mapping between theidentified domain-specific action words to one or more domain-specificactions. Generally, annotating commands in the corpus of known commandsincludes tagging a command in the corpus of known commands with anassociated domain-specific action or otherwise indicating that thecommand is associated with a particular domain-specific action. Forexample, the training data set may include a plurality of commands toinitiate a payment action against a specified entity (e.g., “pay my ABCbill,” “pay my DEF bill,” “pay [amount] to ABC,” pay ABC [amount],” andother commands to pay a bill), and each of the commands may be annotatedwith information identifying “pay” as an action word associated with apayment function provided by a domain-specific service. In some cases,natural language trainer 132 can generate rules associating the word“pay” and variants of the word with the payment function provided by thedomain-specific service. In another example, the training data set mayinclude a plurality of commands to initiate an invoice generationagainst a specified entity (e.g., “invoice ABC for [amount]”) withannotations indicating “invoice” as an action word associated with aninvoice generation function provided by the domain-specific service.Likewise, natural language trainer 132 can generate rules associatingthe word “invoice” and variants of the word with the invoice generationfunction provided by the domain-specific service.

To generate rules for request parser 134 to use in identifyingdomain-specific entities in a received natural language command, thetraining data set used by natural language trainer 132 may include aplurality of known commands annotated with information identifying alocation of an entity in each command and a mapping between theidentified location of an entity and the type of the entity in eachcommand. The annotations may indicate an absolute location in the stringrepresentation of a command at which an entity is located. For example,a training data set may include a plurality of commands to initiate apayment action against a specified entity and, optionally, informationidentifying the amount of the payment to make to the specified entity. Acommand to “pay my ABC bill” may be annotated with informationindicating that the position in which “ABC” appears in the command(i.e., word 3 in the command) is associated with an entity against whichthe payment command is to be executed and that the entity typeassociated with “ABC” is a counterparty to a transaction. In anotherexample, a command to “pay $500 to ABC” may be annotated withinformation identifying two entities in the command. A first entity,represented by “$500” and located at word 2 in the command, may beannotated as a payment amount entity type, and a second entity,represented by “ABC” and located at word 4 in the command, may beannotated as a counterparty entity type. By annotating commands in thecorpus of known commands with information about the location of anentity and the type of the entity at a specific location, naturallanguage trainer 132 can generate a set of rules for request parser 134to use in identifying domain-specific entities in a received naturallanguage command that uses contextual information about a receivedcommand to identify entities in a received command to perform an actionon.

Request parser 134 generally uses the rules established by naturallanguage trainer 132 to analyze received commands and identify an actionto perform in response to a received query and/or one or more entitiesto perform an action on, according to some embodiments. As discussed,request parser 134 can receive a natural language command from anapplication 122 executing on client device 120 as a text stringrepresentation of a spoken command. In some cases, however, requestparser 132 can receive an audio file including a recording of a spokencommand and generate a text string representing the spoken command,which may reduce an amount of time spent processing the received commandat client device 120.

To identify an action to perform in a received natural language command,request parser 134 can, in some cases, compare the received naturallanguage command to commands in the corpus of commands. Request parser134 can generate a match probability score for each pairing of thereceived command and a command in the corpus of commands. The matchprobability score, in some cases, may be a number, edition error, andrecognition error (NER) score indicating an accuracy of the receivedcommand compared to a known command in the corpus of commands.Generally, an NER score may be represented as NER_Score=N-E-R/N where Nrepresents a number of words in the received command, E represents anumber of edition errors compared to a known command, and R represents anumber of recognition errors compared to a known command. An editionerror generally indicates a deviation between a word in the receivedcommand and the corresponding word in the command in the corpus ofcommands. For example, an edition error may arise where the receivedcommand and the known command use the same base word but deviate in theending portion of the word, such as the difference between using a verband a gerund of the same base word (e.g., “pay” versus “paying”). Arecognition error may arise where the received command and the knowncommand use different words, which may arise, for example, in acomparison between different types of commands with different entitiesagainst which the commands are to be executed (e.g., a comparisonbetween “pay my ABC bill” and “invoice XYZ”).

If the match probability score indicates that the received command andthe known command are an exact match (e.g., NER_Score=1.00, indicatingthat no edition errors or recognition errors were detected between thereceived command and the known command), request parser can provide thereceived command and an indication of the action to be performed torequest processor 136 for processing. Otherwise, if request parser 134determines that there is not an exact match between the received commandand any of the commands in the corpus of known commands (e.g., noNER_Score=1.00), request parser 134 can analyze the match probabilityscores to identify the best match between the received command and thecommands in the corpus of known commands. In some cases, request parser134 can identify the command in the corpus of known commands having thehighest match probability score and compare the match probability scoreto a threshold. If the match probability score of the identified commandexceeds a threshold score, request parser 134 can provide the identifiedcommand and the associated action to request processor 136.

In some cases, if the computed match probability score of the closestmatch is less than a threshold score, request parser 134 can, based onthe context of the received natural language command, extract a probableaction word from the received natural language command for analysis. Forexample, request parser 134 can extract the word in a particularposition of the received natural language command based on the positionof action words in other commands in the corpus of known commands. Afterextracting the probable action word from the received natural languagecommand, request parser 134 can compare the extracted probable actionword against known action words in the corpus of known commands andidentify a matching action to the probable action word. Upon identifyinga matching action, request parser 134 provides an indication of thematching action and the received command to request processor forprocessing.

In one example, a domain-specific action may include generating aninvoice. The invoice may be generated for a specified counterparty and aspecified amount. The commands in the corpus of known commands mayinclude a plurality of commands in the format of “invoice counterpartyfor specified amount.” If request parser 134 receives a natural languagecommand in the same format (e.g., “invoice XYZ Company for $500”),request parser 134 can map the received command to the “invoice” actionand instruct request processor 136 to initiate an invoice generationfunction using the parameters included in the received natural languagecommand. If, however, request parser 134 receives a natural languagecommand in a different format, such as “Create an invoice for XYZCompany for $500,” request parser 134 may first determine that theformat of the received natural language command does not match theformat of the invoice generation commands in the corpus of knowncommands. However, based on a word search of the received command,request parser 134 can determine that the word “invoice” or that thephrase “create an invoice” corresponds to a domain-specific action, ascommands in the corpus of known commands include the word “invoice.”Thus, request parser 134 can determine, from the context of the receivedcommand, that the received command corresponds to an invoice generationaction and initiate the invoice generation function using parametersincluded in the received natural language command.

In some cases, if the probability scores calculated for the receivedcommand are each below a threshold value, request parser 134 cantransmit a request for user confirmation of the action having thehighest probability score to client device 120. The request for userconfirmation generally indicates that the request parser identified theaction having the highest probability score as the best match to thenatural language command received from client device 120 and requeststhat the user indicate whether the identified action is correct orincorrect. If the user indicates that the identified action is correct,request parser 134 can add the received natural language command and amapping between the identified action word and the identified action totraining data 142, which may be used to update the rules used by requestparser 134 for identifying actions in natural language commands, asdiscussed above. Otherwise, if the identified action is indicated to beincorrect, request parser 134 can present a list of possible actions tothe user and request that the user select the desired action. Afterrequest parser 134 receives user confirmation that the identified actionis correct or a user-specified action, request parser 134 can providethe identified action or the user-specified action to request processor136 for processing, as discussed above.

Request parser 134 additionally parses received natural languagecommands to identify one or more entities specified in the commandagainst which an action is to be performed, according to an embodiment.Similar to the processes for identifying domain-specific actions to beperformed in a received natural language command, request parser 134 cancompare the received natural language command to a corpus of knowncommands annotated with information identifying the location of anentity in the command and a type of the entity to determine the entityon which the specified action is to be performed. Request parser 134 cangenerate a match probability score for each pairing of the receivedcommand and a command in the corpus of commands. The match probabilityscore, in some cases, may be a number, edition error, and recognitionerror (NER) score indicating an accuracy of the received commandcompared to a known command in the corpus of commands. If the NER scorecomparing the received command to a command in the corpus of knowncommands indicates that the received command is an exact match to aknown command, request parser 134 can extract one or more entities fromthe received command based on the entity location and type mappingsassociated with the matching known command.

Otherwise, if the match probability scores indicate that there is not anexact match between the received command and any of the commands in thecorpus of known commands, request parser 134 attempts to identify one ormore commands in the corpus of known commands that are a partial matchto the received command. For example, if request parser 134 receives thecommand “pay my AMZ bill,” request parser 134 can identify commands suchas “pay my ABC bill” as a partial match (e.g., from an NER score of0.75, reflecting that three of the four words in both commands match)and determine that other commands, such as “invoice ABC $500,” are notpartial matches to the received command (e.g., from an NER score of 0,indicating that none of the words in the commands match) based on thematch probability scores. The commands identified as partial matches,such as “pay my ABC bill,” may be associated with relatively high matchprobability scores, while commands that are not partial matches, such as“invoice ABC $500,” may be associated with relatively low matchprobability scores.

To identify the one or more words that are probable entities in thereceived natural language command, request parser 134 can use the entitytype and location mappings for partially matching commands in the corpusof known commands to extract one or more entities from the receivedcommand. Referring back to the example of the received command “pay myAMZ bill,” the partially matching commands in the corpus of knowncommands generally include commands matching the format of “pay my[entity] bill,” where the entity to which funds are to be paid is acounterparty entity type and is located between the words “my” and“bill,” or at word 3 in the string. Based on the mapping between anentity and the location of the entity, request parser can identify that“AMZ” is the entity on which the payment action is to be performed.

In another example, request parser 134 receives a command to “pay $750to AMZ,” and the corpus of known commands includes commands such as “pay$500 to XYZ,” “pay $500 to ABC,” and similar commands. Each of thesecommands may be mapped to two different entity types at differentlocations: a payment amount entity at word 2 in the string, and thecounterparty entity at word 4 of the string. Using these mappings,request parser 134 can determine that the payment amount in the receivedcommand is $750 and the counterparty to which the payment is to be sentis “AMZ.”

In some cases, if the probability scores calculated for the receivedcommand are each below a threshold value, request parser 134 cantransmit a request for user confirmation of the extracted entities toclient device 120. The request for user confirmation generallyidentifies the words that request parser 134 has identified as probableentities in the natural language command received from client device 120and requests that the user indicate whether the identified entities arecorrect or incorrect. If the user indicates that the identified entitiesare correct, request parser 134 can add the received natural languagecommand and mappings between the location of the identified entities andentity types in the received natural language command to training data142, which may be used to update the rules used by request parser 134for identifying entities in natural language commands, as discussedabove. Otherwise, if the identified entities are indicated to beincorrect, request parser 134 can present a list of possible entities tothe user and request that the user select the desired entities on whichto perform a specified action. After request parser 134 receives userconfirmation that the identified entities are correct or one or moreuser-specified entities, request parser 134 can provide the one or moreentities to request processor 136 for processing, as discussed above.

After request parser 134 extracts the one or more entities from thereceived natural language request, request parser 134 instructs requestprocessor 136 to perform an action specified in the natural languagerequest. Request parser 134 generally provides the one or more entitiesextracted from the received natural language request to requestprocessor 136, and request processor 136 can provide the one or moreentities as arguments into the one or more functions that performs thespecified action.

Request processor 136 generally performs a plurality of domain-specificactions defined by an application programming interface (API). Thesedomain-specific actions, as discussed above, may include paymentfunctions, funds transfer functions, and other functions for whichactions and entities may have a specialized meaning within the domainbut may not have a specialized meaning in more general commandprocessing domains. Generally, request parser 134 provides to requestprocessor 136 an indication of an action to perform and one or moreentities to perform the action on. Request processor 136 uses theindication of the action to perform to execute one or more functionsassociated with the indicated action and uses the one or more entitiesprovided by request parser 134 as arguments into the one or morefunctions such that the action is performed on the identified one ormore entities. After the one or more functions returns a result, requestprocessor 136 can transmit the returned result to client device 120 fordisplay in application 122 (e.g., to confirm that the natural languagecommand provided by the user was executed).

Data store 140 is illustrative of a networked data store thatapplication server 130 can read a training data set from and writeinformation to a training data set for use in training request parser134 to identify actions and entities in domain-specific natural languagecommands, according to an embodiment. As illustrated, data store 140includes training data 142. Training data 142 may be a repositorystoring one or more training data sets for training request parser 134.In some cases, the training data sets may include a plurality of knowncommands and mappings between the plurality of known commands anddomain-specific actions that each command invokes. In other cases, thetraining data sets may include a plurality of known commands andmappings between the plurality of known commands and entities on whichan action can be performed. The mapping data for the entities in acommand may, in some embodiments, include information identifying aposition in a string at which an entity word is located and a type ofthe entity (e.g., a counterparty, a payment amount, or other types ofentities on which domain-specific actions can be performed).

FIG. 2 illustrates an exemplary natural language trainer 132, accordingto an embodiment. As illustrated, natural language trainer 132 includesa training data ingestor 210, an action mapper 220, and an entity mapper230.

Training data ingestor 210 generally obtains a training data set fromdata store 140 and ingests each command in the training data set foranalysis. In some cases, the training data set may indicate to trainingdata ingestor 210 whether the training data includes mappings of knownnatural language commands to domain-specific actions or mappings ofknown natural language commands to domain-specific entities. Based onthe indication, training data ingestor 210 can route the commands in atraining data set to action mapper 220 or entity mapper 230.

Action mapper 220 generally receives one or more commands ingested bytraining data ingestor 210 from an action training data set and analyzesthe mappings between a natural language command and a specified actionto generate rules for identifying an action that is to be performed inresponse to a received natural language command, according to anembodiment. Action mapper 220 may receive a natural language command andan associated domain-specific action, and, to generate a ruleassociating the received natural language command and thedomain-specific action, perform a word search to find a probable actionword in the received natural language command. The probable action word,in some cases, may be the word in the natural language command that hasa highest match score to the domain-specific action. Based on thetraining data, action mapper 220 can generate rules associating probableaction words with a domain-specific action. After generating the rulesfrom an ingested training data set, action mapper 220 can instantiaterequest parser 134 with the determined rules for identifying actions ina received natural language command.

Entity mapper 230 generally receives one or more commands ingested bytraining data ingestor 210 from an entity training data set and analyzesthe mappings between a natural language command and one or morespecified entities to generate rules for identifying entities in areceived natural language command on which one or more actions are to beperformed, according to an embodiment. Entity mapper 230 may receive anatural language command and data indicating the location of an entityand the type of the entity. To generate a rule for recognizing an entityin a natural language command, entity mapper 230 can specify rulesidentifying an absolute location of an entity in a natural languagecommand and rules identifying a location offset between a probableaction word in the natural language command and the specified entity.After generating the rules from an ingested entity training data set,entity mapper 230 can instantiate request parser 134 with the determinedrules for identifying entities in a received natural language command.

FIG. 3 illustrates exemplary operations for training a request parser toidentify one or more domain-specific actions in a received naturallanguage command, according to an embodiment. As illustrated, operations300 begin at step 310, where natural language trainer 132 ingests atraining data set of commands annotated with domain-specific actions. Asdiscussed, the training data set may include a plurality of commands,with each command having an action word and information indicating thatthe command invokes a specified domain-specific action.

At step 320, natural language trainer 132 generate a probabilistic modelmapping key words in each command in the training data set to one ormore domain-specific actions. To generate the probabilistic model,natural language trainer 132 can perform a word-by-word analysis of eachnatural language command in the training data set to identify an actionword in each of the natural language commands that is associated withthe specified domain-specific action. Natural language trainer 132 canaggregate data about the different action words identified in thenatural language commands in the training data set to determine rulesfor calculating the probability that a received command invokes aspecific action. For example, the rules may be established such that anexact match to a specified domain-specific action definitively indicatethat the domain-specific action is to be performed, while variants ofthe action word may be associated with high probability scores that theidentified action word in a command is associated with thedomain-specific action.

At step 330, natural language trainer 132 instantiates a request parserwith the generated probabilistic model. Generally, instantiating therequest parser with the generated probabilistic model includesconfiguring the request parser to use the probabilistic model to analyzeincoming natural language commands for the presence of specifiedactions, as discussed above.

FIG. 4 illustrates exemplary operations for training a request parser toidentify and extract one or more entities on which an action is to beperformed from a received natural language command, according to anembodiment. As illustrated, operations 400 begin at step 410, wherenatural language trainer 132 ingests a training data set of naturallanguage commands. The commands in the training data set are generallyannotated with domain-specific entity information. For example, thecommands may be annotated with information indicating a position of oneor more entity words in the natural language command and a type of theentity.

At step 420, natural language trainer 132 generates a probabilisticmodel mapping key words in each command in the training data set to oneor more domain-specific entities. As discussed above, natural languagetrainer 132 can use the entity position and type information associatedwith each natural language command in the training data set to determinerules for calculating the probability that a received command matchesthe format of a command in the training data set. In some examples, therules may be established such that an exact match to the format of anatural language command in the training data set definitively indicatesthat the entity position information associated with a command in thetraining data set can be used to extract one or more entities from thereceived natural language command. Further, the rules may be establishedsuch that where an exact match between a received natural languagecommand and the commands in the training data set does not exist,entities can be identified based on a relative position of entity wordsto an identified action word in the received natural language command.

At step 430, natural language trainer 132 instantiates a request parserwith the generated probabilistic model. Generally, instantiating therequest parser with the generated probabilistic model includesconfiguring the request parser to use the probabilistic model to analyzeincoming natural language commands for the presence of one or moredomain-specific entities, as discussed above.

FIG. 5 illustrates an example request parser 134 for identifying anaction to perform in response to a received natural language request,according to an embodiment. As illustrated, request parser 134 includesa query comparer 510 and an action probability calculator 520.

Query comparer 510 generally receives a natural language command fromclient device 120 and compares the received natural language command toone or more commands in the corpus of known commands. As discussedabove, query comparer 510 can examine the corpus of known commands forexact matches between the received command and a command in the corpusof known commands. If query comparer 510 identifies an exact matchbetween the received command and a command in the corpus of knowncommands, query comparer 510 can provide the received command andindication of the action associated with the command to requestprocessor 136 for processing, as discussed above. Otherwise, if querycomparer 510 does not find an exact match between the received commandto a command in the corpus of received commands, query comparer 510 caninstruct action probability calculator 520 to determine the most likelymatch between the received command and a command in the corpus ofreceived commands.

Action probability calculator 520 generally compares a received commandto one or more commands in the corpus of known commands to calculate aprobability that an action associated with a command in the corpus ofknown commands corresponds to the action requested in the receivednatural language command. As discussed, action probability calculator520 can generate a match probability score between the received commandand each command in the corpus of known commands to identify an actionassociated with the command having the highest probability of matchingthe received natural language command. In some cases, the matchprobability score may be calculated as an NER score representing therelative accuracy of a received command against a command in the corpusof known commands. If action probability calculator 520 determines thatthe match probability score for a command exceeds a threshold level,action probability calculator 520 can determine that the actionassociated with the command corresponds to the requested action and caninstruct request processor 136 to perform the action associated with thecommand on the entities specified in the received command.

If, however, action probability calculator 520 determines that the matchprobability scores calculated for the commands in the corpus of knowncommands are below the threshold, action probability calculator 520 canattempt to identify an action word in the received command and generatematch probability scores for the identified action word against actionwords associated with commands in the corpus of known commands. Apartial match between the identified action word and an action word inthe corpus of known commands may, in some cases, indicate that theaction associated with an action word in the corpus of known commands isthe action to be performed. In some cases, however, if the probabilityscores calculated for each of the actions in the corpus of knowncommands is less than a threshold score, action probability calculator520 can request that a user indicate, from a choice of a number ofactions having the highest probability scores, the desired action forrequest processor 136 to perform.

FIG. 6 illustrates an example request parser 134 for identifying one ormore domain-specific entities in a received natural language commandbased on a probabilistic model and a comparison to commands in a corpusof known commands, according to an embodiment. As illustrated, requestparser 134 includes a query comparer 610 and an entity probabilitycalculator 620.

Query comparer 610 generally receives a natural language command fromclient device 120 and compares the received natural language command toone or more commands in the corpus of known commands. As discussedabove, query comparer 610 can examine the corpus of known commands forexact matches between the received command and a command in the corpusof known commands. If query comparer 610 identifies an exact matchbetween the received command and a command in the corpus of knowncommands, query comparer 610 can provide the received command andindication of the entities on which to perform a domain-specific actionto request processor 136 for processing, as discussed above. Otherwise,if query comparer 610 does not find an exact match between the receivedcommand to a command in the corpus of received commands, query comparer610 can instruct entity probability calculator 620 to determine the mostlikely match between the received command and a command in the corpus ofreceived commands.

Entity probability calculator 620 generally uses a probabilistic modelgenerated from a corpus of known commands to attempt to identify one ormore entities in the received command. In some cases, entity probabilitycalculator 620 can first determine whether one or more of the commandsin the corpus of known commands comprises a partial match to thereceived natural language command. As discussed above, a partial matchmay be represented as a command having a match probability score (e.g.,an NER score) exceeding a threshold score. If entity probabilitycalculator 620 identifies a partial match in the corpus of knowncommands to the received command, probability calculator 620 can attemptto identify the entities in the received command based on the positionof entities in the partially matching command from the corpus of knowncommands. In some cases, entity probability calculator 620 can identifyan action word in the received command (e.g., by performing a wordsearch of one or more domain-specific actions to words in the receivedcommand) and identify entities on which actions are to be performedbased on a word distance from an action word identified in the receivedcommand.

FIG. 7 illustrates exemplary operations 700 for extractingdomain-specific actions from a natural language command, according to anembodiment. Operations 700 begin at step 710, where an applicationserver 130 receives a natural language command from a client device 120.Natural language application server 130 may receive the natural languagecommand as a text string or an audio file from client device 120. Ifapplication server 130 receives the natural language command from clientdevice 120 as an audio file, natural language application server 130 canconvert the audio file to a text string for processing.

At step 720, application server 130 compares the received command to acorpus of known commands. As discussed, the corpus of known commands mayinclude a plurality of commands, with each command mapped to adomain-specific action (e.g., to pay a bill, transfer funds from oneaccount to another account, generate an invoice to send to a specifiedcounterparty, and other domain-specific actions). For each command inthe corpus of known commands, application server 130 application server130 can calculate a match probability score representing the likelihoodthat the command in the corpus of known commands matches the receivedcommand. As discussed above, the match probability score may, in someembodiments, be calculated as a NER score, which calculates the matchprobability as the percentage of matching words in a command in relationto the total number of words in the command. An NER score of 1 generallyrepresents an exact match between the received command and a command inthe corpus of known commands while an NER score of 0 represents acomplete mismatch between the received command and a command in thecorpus of known commands.

At step 730, application server 130 determines whether any of thecommands in the corpus of known commands is an exact match to thereceived command. If application server 130 determines that an exactmatch exists between the received command and a command in the corpus ofknown commands, at step 740, application server 130 executes the actionassociated with the matching command in the corpus of known commands.

If, however, application server 130 determines that no exact matchexists between the received command and the commands in the corpus ofknown commands, at step 750, application server 130 extracts one or moreaction words from the received natural language command. In some cases,application server 130 can identify action words in the received naturallanguage command based on a word search of words in the natural languagecommand using a listing of domain-specific actions as a dictionary ofwords to search for.

At step 760, application server 130 compares the extracted words toaction words in the corpus of known commands. To compare the extractedwords from the received natural language command to action words in thecorpus of known commands, application server 130 can generate one ormore match probability scores representing a likelihood that theextracted word matches a known action word from the corpus of knowncommands. A high match probability score generally indicates a highlikelihood that the extracted word matches a known action word, while alow match probability score generally indicates that the extracted wordlikely is not a match to a known action word.

At step 770, application server 130 executes the action associated witha command having action words that are the best match to the extractedaction words. A best match may be defined as the action word having thehighest match probability score when compared to an action wordextracted from the received natural language command. In someembodiments, as discussed above, when the highest match probability isbelow a threshold score, application server 130 can request that a userconfirm that the action associated with the highest match probabilityscore is the desired action. If the user confirms that the action withthe highest match probability score is the desired action, applicationserver 130 can execute the action and add an entry to the training dataset associating the extracted action words with the executed action.

FIG. 8 illustrates exemplary operations 800 for extracting one or moreentities from a received natural language command, according to anembodiment. As illustrated, operations 800 begin at step 810, where anapplication server 130 receives a natural language command from a clientdevice 120. Natural language application server 130 may receive thenatural language command as a text string or an audio file from clientdevice 120. If application server 130 receives the natural languagecommand from client device 120 as an audio file, natural languageapplication server 130 can convert the audio file to a text string forprocessing.

At step 820, application server 130 compares the received command to acorpus of known commands. As discussed, the corpus of known commands mayinclude a plurality of commands, with each command mapped to one or moredomain-specific entities (e.g., payment amounts, counterparties, andother entities) and position information indicating where each of theone or more entities is located in the command. The position informationmay be defined as an absolute position in each command and as an offsetfrom an action word in each command. For each command in the corpus ofknown commands, application server 130 application server 130 cancalculate a match probability score representing the likelihood that thecommand in the corpus of known commands matches the received command. Asdiscussed above, the match probability score may, in some embodiments,be calculated as a NER score, which calculates the match probability asthe percentage of matching words in a command in relation to the totalnumber of words in the command. An NER score of 1 generally representsan exact match between the received command and a command in the corpusof known commands while an NER score of 0 represents a complete mismatchbetween the received command and a command in the corpus of knowncommands.

At step 830, application server 130 determines whether any of thecommands in the corpus of known commands is an exact match to thereceived command. If application server 130 determines that an exactmatch exists between the received command and a command in the corpus ofknown commands, at step 840, application server 130 extracts an entityfrom the received command based on a position associated with an entitytype in the matching command. At step 850, application server 130executes an action specified in the received command on the entityidentified in the command.

If, at step 830, application server 130 determines that no command inthe corpus of known commands is an exact match to the received command,at step 860, application server 130 identifies a location of a probableentity based on a context of the received command similar commands inthe corpus of known commands. In some cases, application server 130 candetermine if a command in the corpus of known commands is a partialmatch to the received command. If so, application server 130 can useabsolute position information from the partially matching command in thecorpus of known commands to identify the location of probable entitiesin the received natural language command. If, however, applicationserver 130 determines that none of the commands in the corpus are asufficient partial match to the received command, application server 130can use context information identify one or more probable matchingcommands, such as a command in the corpus of known commands that isassociated with the same action as that specified in the receivedcommand. Based on the identified probable matching commands, applicationserver 130 can use a relative distance from an action word to identifyone or more entities in the received natural language command. At step870, application server 870 executes an action specified in the receivedcommand on the probable entities identified in the received command.

FIG. 9 illustrates a system 900 that extracts domain-specific actionsand entities from a received natural language command, according to anembodiment. As shown, the system 1000 includes, without limitation, acentral processing unit (CPU) 902, one or more I/O device interfaces 904which may allow for the connection of various I/O devices 914 (e.g.,keyboards, displays, mouse devices, pen input, etc.) to the system 900,network interface 906, a memory 908, storage 910, and an interconnect912.

CPU 902 may retrieve and execute programming instructions stored in thememory 908. Similarly, the CPU 902 may retrieve and store applicationdata residing in the memory 908. The interconnect 912 transmitsprogramming instructions and application data, among the CPU 902, I/Odevice interface 904, network interface 906, memory 908, and storage910. CPU 902 is included to be representative of a single CPU, multipleCPUs, a single CPU having multiple processing cores, and the like.Additionally, the memory 908 is included to be representative of arandom access memory. Furthermore, the storage 910 may be a disk drive,solid state drive, or a collection of storage devices distributed acrossmultiple storage systems. Although shown as a single unit, the storage1010 may be a combination of fixed and/or removable storage devices,such as fixed disc drives, removable memory cards or optical storage,network attached storage (NAS), or a storage area-network (SAN).

As shown, memory 908 includes a natural language trainer 920, a requestparser 930, and a request processor 940. Natural language trainer 920generally uses one or more training data sets (e.g., stored in trainingdata 950) to generate a set of rules for extracting domain-specificactions and domain-specific entities from a received natural languagecommand. The training data sets may be annotated with informationidentifying an action associated with each command in the training dataset and a position of one or more entities in each command. By analyzingthe commands and the annotations associated with each command, naturallanguage trainer 920 can generate one or more rules or a probabilisticmodel that request parser 930 can use to identify domain-specificactions and domain-specific entities in a received natural languagecommand.

Request parser 930 generally receives a natural language command from aclient device 120 and, using the rules and probabilistic model generatedby natural language trainer 920, extracts domain-specific actions anddomain-specific entities from the natural language command. Requestparser 930 receives natural language commands from a client device 120as a text string or an audio file which request parser converts to atext string for processing. Using the text string, request parser 930generally compares the received natural language command to commands ina corpus of known commands to attempt to find an exact match to thereceived command. If request parser 930 finds an exact match to thereceived command, request parser 930 extracts a domain-specific actionfrom the received command using the domain-specific action mapped to thematching command in the corpus of known commands. Request parser 930additionally extracts one or more domain-specific entities from thereceived command based on location information and entity typeinformation associated with the matching command.

If, however, request parser 930 does not find an exact match to thereceived command, request parser 930 searches for partial matchesbetween the received command and the commands in the corpus of knowncommands to extract domain-specific actions and entities from thereceived command. In some cases, request parser 930 can use a matchprobability score to identify one or more partially matching commands inthe corpus of known commands. If request parser 930 finds one or morepartially matching commands with a match probability score that exceedsa threshold value, request parser 930 can extract domain-specificactions and entities from the received command based on actions mappedto the partially matching commands and the locations of entitiesassociated with the partially matching command.

In some cases, request parser 930 uses contextual information to extractdomain-specific actions and entities from a received natural languagecommand. As discussed, request parser 930 can identify an action word ina received natural language command, which may be a verb, and comparethe identified action word to action words in the corpus of knowncommands to identify an action specified by the natural languagecommand. In another example, request parser 930 can identify an actionword in a received natural language command, identify one or morecommands in the corpus of known commands associated with the identifiedaction word, and use relative position information to extract one ormore entities from the received natural language command.

Request processor 940 generally performs a plurality of domain-specificactions defined by an application programming interface (API). Thesedomain-specific actions, as discussed above, may include paymentfunctions, funds transfer functions, and other functions for whichactions and entities may have a specialized meaning within the domainbut may not have a specialized meaning in more general commandprocessing domains. Generally, request parser 930 provides to requestprocessor 940 an indication of an action to perform and one or moreentities to perform the action on. Request processor 136 uses theindication of the action to perform to execute one or more functionsassociated with the indicated action and uses the one or more entitiesprovided by request parser 930 as arguments into the one or morefunctions such that the action is performed on the identified one ormore entities. After the one or more functions returns a result, requestprocessor 940 can transmit the returned result to client device 120 fordisplay in application 122.

As shown, storage 910 includes training data 950. Training data 950 maybe a repository storing one or more training data sets for trainingrequest parser 930. The training data sets may include a plurality ofknown commands and mappings between the plurality of known commands anddomain-specific actions that each command invokes. In other cases, thetraining data sets may include a plurality of known commands andmappings between the plurality of known commands and entities on whichan action can be performed. The mapping data for the entities in acommand may, in some embodiments, include information identifying aposition in a string at which an entity word is located and a type ofthe entity (e.g., a counterparty, a payment amount, or other types ofentities on which domain-specific actions can be performed).

Advantageously, by training a natural language processing system torecognize domain-specific actions and domain-specific entities, anatural language processing system can expose conversational userinterfaces to a user of a client device to perform specialized functionsthat would otherwise not be understandable to general-purpose naturallanguage processing systems. Because actions and entities indomain-specific applications may have specialized meanings within thedomain, a natural language processing system trained usingdomain-specific information can recognize the significance of theactions and entities in a natural language command and invoke one ormore domain-specific functions to perform the specialized actions on theentities identified in received natural language commands.

Note, descriptions of embodiments of the present disclosure arepresented above for purposes of illustration, but embodiments of thepresent disclosure are not intended to be limited to any of thedisclosed embodiments. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope and spirit of the described embodiments. The terminology usedherein was chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

In the preceding, reference is made to embodiments presented in thisdisclosure. However, the scope of the present disclosure is not limitedto specific described embodiments. Instead, any combination of thepreceding features and elements, whether related to differentembodiments or not, is contemplated to implement and practicecontemplated embodiments. Furthermore, although embodiments disclosedherein may achieve advantages over other possible solutions or over theprior art, whether or not a particular advantage is achieved by a givenembodiment is not limiting of the scope of the present disclosure. Thus,the aspects, features, embodiments and advantages discussed herein aremerely illustrative and are not considered elements or limitations ofthe appended claims except where explicitly recited in a claim(s).Likewise, reference to “the invention” shall not be construed as ageneralization of any inventive subject matter disclosed herein andshall not be considered to be an element or limitation of the appendedclaims except where explicitly recited in a claim(s).

Aspects of the present disclosure may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore,aspects of the present disclosure may take the form of a computerprogram product embodied in one or more computer readable medium(s)having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples a computer readable storage medium include: anelectrical connection having one or more wires, a hard disk, a randomaccess memory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), an optical fiber, a portablecompact disc read-only memory (CD-ROM), an optical storage device, amagnetic storage device, or any suitable combination of the foregoing.In the current context, a computer readable storage medium may be anytangible medium that can contain, or store a program.

While the foregoing is directed to embodiments of the presentdisclosure, other and further embodiments of the disclosure may bedevised without departing from the basic scope thereof, and the scopethereof is determined by the claims that follow.

What is claimed is:
 1. A method for processing natural languagecommands, comprising: receiving a natural language command; comparingthe received natural language command to a corpus of known commands toidentify a probable matching command in the corpus of known commands tothe received natural language command, wherein the corpus of knowncommands comprises a plurality of domain-specific commands, each of theplurality of domain-specific commands being mapped to a domain-specificaction; based on the comparison, identifying the domain-specific actionassociated with the probable matching command to perform in response tothe received natural language command; and executing the identifieddomain-specific action.
 2. The method of claim 1, wherein the mappingbetween a command in the plurality of domain-specific commands todomain-specific actions comprises a mapping between an action word inthe domain-specific command and a domain-specific action.
 3. The methodof claim 1, wherein identifying the domain-specific action to perform inresponse to the received command comprises: identifying an exact matchbetween the received natural language command and a command in thecorpus of known commands; and executing a domain-specific action mappedto the matching command in the corpus of known commands.
 4. The methodof claim 1, wherein identifying the domain-specific action to perform inresponse to the received command comprises: identifying one or morepartial matches between the received natural language command and thecommands in the corpus of known commands; and selecting one of the oneor more partial matches as a probable matching domain-specific command.5. The method of claim 4, wherein each of the one or more partialmatches is associated with a probability score indicating a likelihoodthat the domain-specific action associated with a partial matchcorresponds to an action requested by the user, and wherein selectingone of the one or more partial matches comprises selecting the partialmatch with a highest probability score.
 6. The method of claim 5,further comprising: upon determining that the probability scoreassociated with the identified partial match exceeds a thresholdprobability score, executing the domain-specific action associated withthe identified partial match.
 7. The method of claim 5, furthercomprising: upon determining that the probability score associated withthe identified partial match is less than a threshold probability score:prompting the user to indicate whether the domain-specific actionassociated with the partial match is correct; and upon receiving anindication that the domain-specific action is correct, executing thedomain-specific action associated with the identified partial match. 8.The method of claim 5, wherein the probability score is based, at leastin part, on an error rate calculated from a comparison between thereceived natural language command an each of the commands in the corpusof known commands.
 9. The method of claim 1, further comprising:receiving a training data set including a plurality of domain-specificcommands annotated with information identifying a domain-specific actionassociated with each of the plurality of domain-specific commands; andgenerating one or more rules to identify domain-specific actions inreceived commands based on the training data set.
 10. A system,comprising: a processor; and a memory having instructions stored thereonwhich, when executed by the processor, performs an operation forprocessing natural language commands, the operation comprising:receiving a natural language command; comparing the received naturallanguage command to a corpus of known commands to identify a probablematching command in the corpus of known commands to the received naturallanguage command, wherein the corpus of known commands comprises aplurality of domain-specific commands, each of the plurality ofdomain-specific commands being mapped to a domain-specific action; basedon the comparison, identifying the domain-specific action associatedwith the probable matching command to perform in response to thereceived command; and executing the identified domain-specific action.11. The system of claim 10, wherein the mapping between a command in theplurality of domain-specific commands to domain-specific actionscomprises a mapping between an action word in the domain-specificcommand and a domain-specific action.
 12. The system of claim 10,wherein identifying the domain-specific action to perform in response tothe received command comprises: identifying an exact match between thereceived natural language command and a command in the corpus of knowncommands; and executing a domain-specific action mapped to the matchingcommand in the corpus of known commands.
 13. The system of claim 10,wherein identifying the domain-specific action to perform in response tothe received command comprises: identifying one or more partial matchesbetween the received natural language command and the commands in thecorpus of known commands; and selecting one of the one or more partialmatches as a probable matching domain-specific command.
 14. The systemof claim 13, wherein each of the one or more partial matches isassociated with a probability score indicating a likelihood that thedomain-specific action associated with a partial match corresponds to anaction requested by the user, and wherein selecting one of the one ormore partial matches comprises selecting the partial match with ahighest probability score.
 15. The system of claim 14, furthercomprising: upon determining that the probability score associated withthe identified partial match exceeds a threshold probability score,executing the domain-specific action associated with the identifiedpartial match.
 16. The system of claim 14, further comprising: upondetermining that the probability score associated with the identifiedpartial match is less than a threshold probability score: prompting theuser to indicate whether the domain-specific action associated with thepartial match is correct; and upon receiving an indication that thedomain-specific action is correct, executing the domain-specific actionassociated with the identified partial match.
 17. The system of claim14, wherein the probability score is based, at least in part, on anerror rate calculated from a comparison between the received naturallanguage command an each of the commands in the corpus of knowncommands.
 18. The system of claim 10, further comprising: receiving atraining data set including a plurality of domain-specific commandsannotated with information identifying a domain-specific actionassociated with each of the plurality of domain-specific commands; andgenerating one or more rules to identify domain-specific actions inreceived commands based on the training data set.
 19. Acomputer-readable medium having instructions stored thereon, which, whenexecuted by a processor, performs an action for processing naturallanguage commands, the operation comprising: receiving a naturallanguage command; comparing the received natural language command to acorpus of known commands to identify a probable matching command in thecorpus of known commands to the received natural language command,wherein the corpus of known commands comprises a plurality ofdomain-specific commands, each of the plurality of domain-specificcommands being mapped to a domain-specific action; based on thecomparison, identifying the domain-specific action associated with theprobable matching command to perform in response to the receivedcommand; and executing the identified domain-specific action.
 20. Thecomputer-readable medium of claim 19, wherein the operation furthercomprises: receiving a training data set including a plurality ofdomain-specific commands annotated with information identifying adomain-specific action associated with each of the plurality ofdomain-specific commands; and generating one or more rules to identifydomain-specific actions in received commands based on the training dataset.